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Social capital, team efficacy and team potency The mediating role of team learning behaviors

82 Received 16 August 2010 Revised 16 September 2010 Accepted 16 September 2010

Hetty van Emmerik Department of Organization and Strategy, Maastricht University School of Business and Economics, Maastricht, The Netherlands

I.M. Jawahar Department of Management & Quantitative Methods, Illinois State University, Normal, Illinois, USA

Bert Schreurs Centre for Corporate Sustainability, European University College Brussels, Brussels, Belgium, and

Nele de Cuyper Department of Organizational Psychology, University of Leuven, Leuven, Belgium Abstract

Career Development International Vol. 16 No. 1, 2011 pp. 82-99 q Emerald Group Publishing Limited 1362-0436 DOI 10.1108/13620431111107829

Purpose – Drawing on social capital theory and self-identification theory, this study aims to examine the associations of two indicators of social capital, personal networks and deep-level similarity, with team capability measures of team efficacy and team potency. The central focus of the study is to be the hypothesized mediating role of team learning behaviors. Design/methodology/approach – Hypotheses were tested using questionnaire data obtained from 221 teachers working in 33 teams and data were analyzed using multilevel analyses. Findings – Consistent with the hypotheses, the results supported the contention that team learning behaviors mediate the relationship between different types of social capital and team efficacy and team potency. Specifically, it was found that, in highly (deep-level) similar teams, the level of team learning behaviors is higher than in diverse teams, and this is hardly dependent on the extent of social capital based on personal networks. For diverse teams (i.e. teams scoring low on deep-level similarity) more social capital based on personal networks translates into more team learning behaviors. Finally, it was found that team learning behaviors mediate the influence of social capital on team efficacy and team potency. Research limitations/implications – The paper’s findings suggest that it is important for managers not to focus exclusively on surface level characteristics but instead to attempt to facilitate the development of deep-level similarity. Organizations can also encourage group social capital by allowing teams to develop a shared history, rather than change membership frequently, and by increasing contact among team members. Originality/value – The paper examined exchange and identification processes that are important in generating resources to increase the development of team learning behaviors, thereby emphasizing the role of the interpersonal context for understanding how interaction processes between team members shape team learning behaviors and subsequently lead to more team efficacy and team potency. Keywords Social networks, Team learning, Team working Paper type Research paper

Introduction Teams represent the basic work units in many organizations today, so much so that effective functioning of organizations is heavily dependent upon the capabilities of teams (Cummings and Worley, 2005; Jex, 2002; Mathieu et al., 2008; Rowe, 1996). Team efficacy and team potency are important indicators of capabilities of teams (Mathieu et al. 2008). Team efficacy, originating from Bandura’s (1997a) self-efficacy theory, concerns the team’s perceived capability to organize and execute courses of action required to produce given levels of goal attainment (Kozlowski and Ilgen, 2006). Team potency is a relatively broader construct and refers to “the generalized beliefs about capabilities of the team across tasks and contexts” (Gibson, 1999; Shea and Guzzo, 1987). Given the growing significance of work teams in contemporary organizations, it is important to examine how such capabilities can be enhanced by studying antecedents, and the mechanism through, which such antecedents influence team efficacy and team potency. The question as to how team capabilities can be enhanced is twofold: first, it refers to the examination of possible antecedents of team efficacy and team potency. We propose that opportunities to use social capital from relationships within the team as one relevant class of antecedents. Drawing on social capital theory, we use personal networks and deep-level similarity as indicators of social capital. According to social capital theory, networks of relationships constitute valuable resources providing members with a kind of “collectivity-owned capital” (Nahapiet and Ghoshal, 1998). Much of this capital is embedded within networks of mutual acquaintance and in the present study is argued to be manifested in personal networks and in perceptions of deep-level similarity (Nahapiet and Ghoshal, 1998; Raider and Burt, 1996; Sandefur and Laumann, 1998; Seibert et al., 2001). Second, the question as to how team capabilities can be enhanced has implications for identifying mechanisms that underlie the relationship between social capital and team efficacy and team potency. We assert that team learning behaviors mediate the relationship between social capital and team capabilities. Team learning behaviors are behaviors through which team members seek to acquire, share, refine, or combine task-relevant knowledge through interaction with one another (Van Der Vegt and Bunderson, 2005). Our assertion is consistent with models of team effectiveness that afford a key role for team processes (e.g. Gist et al., 1987; Guzzo and Shea, 1992) and with Edmondson’s (1999) proposed model of team learning, wherein team learning is conceptualized as behaviors such as seeking feedback, sharing information, experimenting, asking for help, and talking about errors. The central argument we advance is that opportunities to generate social capital from personal networks and perceptions of deep-level similarity within teams translate into positive team learning behaviors, which in turn, relate to team capabilities as indexed by team efficacy and team potency (Figure 1). Stated differently, team learning behaviors are hypothesized to mediate the relationship between social capital and assessment of capabilities in a team environment. We test this model using longitudinal data obtained from teachers working within teams in Dutch secondary schools. Similar to professionals in work teams, teachers are knowledge workers who need to collaborate with each other to create a quality product (see also Van der Heijden, 2001). In addition to the theoretical contributions, methodological contributions of this study include the multi-level analysis of data collected from multiple sources.

Team learning behaviors

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84 Figure 1. The moderating role of deep level similarity on the association between social capital and team learning behaviors

1. Team capability: team efficacy and team potency A team refers to a “small number of people with complementary skills who are committed to a common purpose, set of performance goals, and approach for which they hold themselves mutually accountable” (Katzenbach and Smith, 1993, p. 45). At the heart of this definition lies the fundamental premise that teams and team capabilities are inextricably linked (Katzenbach and Smith, 1993). Team confidence or the shared beliefs of team members about the ability of the team to perform successfully can be studied as team efficacy and team potency (Mathieu et al., 2008; Spielberger, 2002). Previous studies have suggested that task specific team efficacy (Gist et al., 1987; Lindsley et al., 1995) and the more generalized concept of team potency (Guzzo et al., 1993) are important determinants of team performance. Team efficacy refers to perceptions that the team can succeed at specific tasks and team potency refers to perceptions of general team capabilities that span across many tasks and situations (Gibson, 1999; Gully et al., 2002; Kennedy et al., 2009). While both team efficacy and team potency are linked to team effectiveness (Gully et al., 2002), previous research has established team efficacy and team potency as distinct constructs (Gibson, 1999; Gully et al., 2002). 2. Team learning behaviors and team capability There is abundant support for the importance of building teams and being a team player (Longenecker and Fink, 2008) and the contention that interpersonal processes relate positively to team performance (e.g. Van Emmerik, 2008, Bradley et al., 2003; Zellmer-Bruhn and Gibson, 2006). In our study, we focus specifically on the role of team learning behaviors and argue that social capital will have a positive influence on team learning behaviors, and that team learning behaviors, in turn, will enhance overall perceptions by team members of team efficacy and team potency. While previous research has not investigated these relationships, a study by Van Der Vegt and Bunderson (2005) offers indirect support for the general idea. They reported that team learning behaviors (partially) mediated the associations between specific types of social resources (i.e. expertise diversity – differences in knowledge

and skills in which team members are specialized as a result of their work experience and education) and team performance. In another study, Edmonson (1999) reported that when team members engage in learning-related behaviors, a team learning climate develops wherein the team improves its ability to operate effectively within the context of the larger environment (e.g. Edmondson, 1999). In addition to the indirect support from the aforementioned empirical studies, we draw on two ideas to assert that teams that engage in learning behaviors and learn as a team are likely to perceive high levels of team efficacy and team potency than teams that do so less often. The first is the Input-Process-Output model formulated by McGrath (McGrath, 1984) in which “process” mediates the relationship between input and output. Team learning behaviors are process focused and consequently, effective team learning behaviors should give rise to an enhanced collective assessment of team efficacy and team potency. Second, extending the social learning perspective of Bandura (1997b) to the team perspective, it can be argued that efficacy judgments will be influenced by past experiences of the team. Thus, teams that engage in learning experiences are likely to develop a collective sense of competence. 3. Personal network and deep level similarity as antecedents to team capability We draw on Social Capital Theory to suggest that social capital generated through personal networks within a team will enhance team learning behaviors. Next, we draw on Social Identification Theory (Tajfel, 1982) to assert that deep-level similarity, a form of social capital, will enhance team learning behaviors. In turn, team learning behaviors are expected to contribute to enhanced perceptions of team efficacy and team potency. Social Capital Theory. Social Capital Theory provides an avenue to understand the relationship between indicators of social capital and team learning behaviors. Social capital created in networks of groups or teams refers to the “goodwill that is engendered by the fabric of social relations and that can be mobilized to facilitate action” (Adler and Kwon, 2002, p. 17). Social capital may provide members various benefits, such as information, influence, and control. Development of social capital takes place in the work-related social network of each employee, i.e. in personal networks. Work-related networks are relationships between employees through which they share resources such as information, assistance, and guidance that are related to the completion of their work (Sparrowe et al., 2001). The contents of these relationships between colleagues contain elements of friendship, developmental relationships, and information exchange. Important types of relationships in a social network within the working context are personal, advice giving, and advice receiving relationships and personal relationships. Personal networks are sets of people who are highly salient and important to a person and who have the most influence on a person’s attitudes, behavior, and well-being (see Straits, 2000). According to Adler and Kwon (2002) personal networks refer to the social relational dimension of social capital, and a higher level of social capital will likely be associated with more team learning behaviors. In addition, stronger personal networks (e.g. in terms of more similarity within the network) can be expected to enhance team’s collective capability to organize and execute courses of action (team efficacy) and also contribute to generalized beliefs about capabilities of the team (team potency). Thus, social capital indexed as personal networks is expected to directly relate to team


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efficacy and team potency and also indirectly through its effect on team learning behaviors: H1. The positive association of personal networks with team efficacy and team potency will be partially mediated by team learning behaviors.

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Social Identity Theory. We draw on Social Identity Theory (SIT) (Tajfel, 1982; Van Dick et al., 2005) to understand the relationships between deep-level similarity, team learning behaviors, and team efficacy and team potency. Social identification refers to identifying others as being a member of a certain category (i.e. belonging to a category such as being male or being female) and identifying with that category (i.e. perceiving that category is relevant for one’s own identity). In the context of this study, the focus is on opportunities to generate resources from interacting with similar others to predict team learning behaviors and team efficacy and potency. According to Bell (2007), team composition can have a strong influence on team learning behaviors. Team composition can be viewed from a surface-level characteristics perspective or a deep-level characteristics perspective. Surface-level similarity refers to similarities among employees in terms of overt demographic characteristics. Most frequently, these similarities are reflected in directly observable characteristics, such as age, sex, and race or ethnicity (Gellert and Kuipers, 2008; Harrison et al., 1998). Employees assess others’ age, gender, race or ethnic background and estimate similarity or dissimilarity to themselves, and use this assessment to assign themselves and others to some kind of social classification (Harrison et al., 1998). However, it is not surface-level but deep-level similarity that paves the way to generating social resources. Deep-level similarity refers to similarities among employees’ psychological characteristics, including personality, values, and attitudes. Deep-level similarity increases ease of communication, improves predictability of behavior, and fosters relationships of trust and reciprocity (Ibarra, 1995). Indeed, research by Harrison and his colleagues (Harrison et al., 1998; Harrison et al., 2002) reported that the influence of deep-level similarity and differences increases with time, whereas the influence of surface-level characteristics diminishes over time. According to SIT (Tajfel, 1982; Van Dick et al., 2005), individuals classify others as being a member of a certain group and identify with groups similar to oneself. We assert that in established ongoing teams, deep-level similarities will be the primary currency of identification, which in turn, will reinforce perceptions of similarities and pride in group membership. The recognition, cohesion and feelings of emotional well-being resulting from deep-level similarities (see Van Dick et al., 2005) will contribute to social capital and encourage more team learning behaviors and also contribute to perceptions of team efficacy and team potency. Deep-level similarity indexed as social capital is expected to directly relate to team efficacy and team potency and also indirectly through its effect on team learning behaviors. H2. The positive association of deep level similarity with team efficacy and team potency will be partially mediated by team learning behaviors. As previously argued, personal networks facilitate social capital that is expected to facilitate team learning behaviors. Perceptions of deep-level similarity lead to trust, liking and cohesiveness. Thus, deep-level similarity could accentuate the effects of personal networks on team learning behaviors. Stated differently, when people in

personal networks also perceive a high level of deep-level similarity, such perceptions will interact with and enhance the effects of personal network on team learning behaviors. We advance the following hypothesis to capture these interactive effects. H3. Social capital based on deep-level similarity will interact with social capital based on personal networks to influence team learning behaviors, such that the association between personal network and team learning behaviors will be stronger at higher levels of perceived deep-level similarity than at lower levels of perceived deep-level similarity. Method Sample Data were collected from teachers working within Dutch secondary schools. The data collection was a part of a research project investigating the adaptation after organizational restructuring. The restructuring involved the implementation of student-centered cross-functional teams within schools. Student-centered cross-functional teams require employees from different functional areas to work together as tightly integrated units toward the accomplishment of common educational goals (Schelfhaudt and Crittenden, 2005). This change initiative within secondary schools was initiated to improve quality by adopting a student-centered approach. Fourteen schools (out of 16 approached schools) participated in the 2007 wave of data collection and they all went through the organizational restructuring effort to work in student-centered cross-functional teams. School management and/or principals of the 14 schools were approached. Next, school management announced the study to the team leaders and the team leaders explained the purpose of the study and solicited participation in the study. A total 52 out of the 66 (79 percent) written questionnaires that were sent to the team leaders were returned. A total of 1,049 written questionnaires were sent to the teachers who were housed in one of 52 teams. Of these, 442 were returned, resulting in a response rate of 42 percent. For the present study, we selected those teams that were rated by a representative of the school management of the 14 schools, which resulted in data from 221 teachers within 33 teams. This sample consisted of 110 male (50 percent) and 111 female (40 percent) teachers. Mean age of the respondents was 41.2 years (SD 11.6). Mean team size of the 33 teams is 9.9 team members (SD ¼ 5:2). Measures Team capabilities – team efficacy. Team efficacy was measured using a scale from Janssen et al. (1999). This scale includes four items that refer to team efficacy. A sample item is “Decisions taken by our team have a positive effect on the performance of our organization”. Items were scored on a five-point scale, ranging from 1 completely disagree to 5 completely agree. Reliability (coefficient alpha) was 0.81. Team capabitities – team potency. Team potency was measured with the five items selected by Gevers (2004) from the original scale by Guzzo et al. (1993). A sample item is “This team has confidence in itself”. Items were scored on a five-point scale, ranging from 1 completely disagree to 5 completely agree. Reliability (coefficient alpha) was 0.84. Team learning behaviors. Team learning behaviors were rated per team on a ten-point response scale (1 representing poor and 10 representing excellent) using nine


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items from Beehr et al. (Delivery of Results, 2001) aimed at measuring team learning behaviors. Sample items are “This team solves problems on its own when appropriate” and “Learns from mistakes.” Reliability (coefficient alpha) was 0.90. To rule out common method bias as an explanation for relationships with team capabilities as perceived by the respondents, we relied on ratings of the team learning behaviors by a representative of the school’s management team. Thus, these team learning behaviors scores captured a group-level phenomenon. Social capital based on deep-level similarity. Social capital in the form of deep-level similarity was measured by a scale from Turban et al. (2002). These items measure the extent to which teachers believe that their team members are similar in terms of underlying attitudes, values, and beliefs, reflecting a deeper level similarity. A sample item is “In our team, we see things in much the same way”. Reliability of this scale was 0.78. Social capital based on personal network. Social capital in the form of the personal network was measured with items adapted from Klein et al. (2004). These items were preceded by the following sentences: “The following question concerns your relationships with other team members. How many of your colleagues in your present team can you count on?” The Social Capital – Personal Network score was computed from counting the number of team members (1) whom they went to consult on personal matters; (2) whom they considered friends, and (3) with whom they socialized after work hours. Control variables. We controlled for gender (0 male, 1 female) and age (in years). The underlying proposition is that gender and age, which are salient demographic characteristics, represent surface-level similarity (Harrison et al., 2002). Further, size of social groups has been shown to affect the magnitude of several other group phenomena. For instance, since liking and performance declines with increasing team size (see Salas et al., 1999), we also controlled for team size. Results Table I presents the means, standard deviations, and bivariate correlations for individual level and team level variables. The correlations reported in Table I are generally consistent with the hypotheses. For example, positive and significant correlations were found between team learning behaviors and personal network (r ¼ 0:20, p , 0:01) and deep-level similarity (r ¼ 0:49, p , 0:01). While the correlation between team efficacy and team potency is fairly high (r ¼ 0:58, p , 0:01), it is consistent with that reported in previous studies (Gully et al., 2002). To test the hypotheses, we used two types of analyses. The data for learning behaviors were collected at the team level and not at the individual level. School management assessed team learning behaviors for each team and not for each team member. Thus, when team learning behavior was the dependent variable, we used OLS regression. Second, with team efficacy and team potency as dependent variables, data were at the team and at the individual level and multilevel null models were estimated. ICC values and associated F-tests for the null-models revealed that 41 percent of the variance in team efficacy resided between teams (F ¼ 4:06½DF ¼ 232, p , 0:01), and 28 percent of the variance in team potency resided between teams (F ¼ 2:33½DF ¼ 320, p , 0:01). Hausman tests (Hausman, 1978) were not significant for both dependent measures indicating that a random effects model and

Gender Age Team size Team efficacy Team potency Team learning behaviors Personal network Deep level similarity

Notes: * p , 0.05; * * p , 0.01

1 2 3 4 5 6 7 8

0.50 41.22 9.89 3.41 3.54 7.20 1.48 3.06

Mean 0.50 11.55 5.24 0.67 0.64 0.78 0.59 0.26

SD 20.28 * * 0.09 0.13 0.08 0.02 0.06 0.17 *

1

20.04 20.24 * * 20.19 * * 0.04 20.17 * 20.20 * *

2

0.01 0.04 2 0.27 * * 0.25 * * 2 0.02

3

0.58 * * 0.36 * * 0.16 * 0.38 * *

4

0.28 * * 0.22 * * 0.36 * *

5

7

0.22 * *

6

0.20 * * 0.49 * *


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Table I. Means, standard deviations and correlations (n ¼ 221, n of teams ¼ 33)

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not a fixed effects model is the preferred type of multilevel analyses. Accordingly, for this part of tests of hypotheses, we performed random-effects multilevel analyses. Since personal network and deep-level similarity were aggregated to team level, we calculated the inter-rater reliability using multi-rater kappa (an unweighted form of kappa). Although there are different methodologies for the assessment of observer agreement, in the present study we used Kappa because Kappa can handle cases where there are more than three categories to be rated (the personal network measure consisted of count data). According to Altman (1991) interpretation of Kappa is: poor agreement 0.20, fair agreement 0.20 to 0.40, moderate agreement 0.40 to 0.60, good agreement 0.60 to 0.80, and very good agreement 0.80 to 1.00. Kappa’s for the aggregated measures included in the present study indicated fair agreement for both social capital – personal network (Kappa ¼ 0:374, p , 0:01) and deep-level similarity (Kappa ¼ 0:238, p , 0:01). We tested the hypotheses by entering the control variables in the first step, the two main effects (personal network and deep-level similarity as independent variables) in the second step, and the mediator (team learning behaviors) in the third step. The results for our mediation analyses are presented in Table II.

Dependent variable Team learning behaviors g SE Intercept null model

Table II. Results of OLS regression (for team learning behaviors) and random-effects GLS regression for team effectiveness and team potency (n ¼ 221, n of teams ¼ 33)

Team efficacy g SE

Team potency g SE

3.431 * *

0.078

3.531 * *

0.064

Step 1: Only control variables Gender Age Size of the team

0.090 0.003 20.040 * *

0.107 0.005 0.010

0.059 20.011 * * 0.003

0.081 0.004 0.016

0.006 20.009 * 0.009

0.085 0.003 0.014

Step 2: Mediator excluded Gender Age Size of the team Deep level similarity Social capital

20.000 0.010 * 20.043 * * 1.378 * * 0.173 *

0.092 0.004 0.009 0.184 0.083

0.043 20.009 * 20.003 0.739 * * 0.035

0.081 0.004 0.015 0.237 0.112

20.013 20.007 0.004 0.732 * * 0.093

0.084 0.004 0.010 0.182 0.083

0.041 20.010 * * 0.003 0.246 * * 0.466 0.002

0.081 0.004 0.013 0.089 0.240 0.102

20.013 20.008 * 0.009 0.155 * 0.549 * * 0.056

0.083 0.004 0.009 0.155 0.549 0.056

0.270

0.268

0.357

0.213

Step 3: Mediator included Gender Age Size of the team Team learning behaviors Deep level similarity Social capital Step 4: interaction added Deep level similarity * Social capital R 2 total model Wald Chi2

20.592 * * 0.332 * *

0.229

0.235 * * 36.82

0.188 * * 49.2

Notes: In Step 4 we only report the interaction coefficients and omitted the results of the main effects; * p , 0.05; * * p , 0.01

Our hypotheses predicted that team learning behaviors would serve as a mediator between exchange and identification processes and team capabilities. These predictions can be tested following Baron and Kenny’s (1986) four step approach. In Step 1, the independent variables should be significantly related to the mediator. In the second step, the independent variables should be related to the dependent variables. In the third step, the mediator should be correlated with the dependent variables while controlling for independent variables. Finally, in Step 4, the effect of the independent variables on the dependent variables should be zero when controlling for the mediator. As shown in Table II, Step 1 was satisfied for both mediators, as personal network (b ¼ 0:173, p , 0:05) and deep-level similarity (b ¼ 1:378, p , 0:01) were significantly related to team learning behaviors. Step 2 in the mediation analysis tested the relationship between the independent and dependent variables. Results, also shown in Table II, demonstrate that deep-level similarity was significantly related to both team efficacy (g ¼ 0:739, p , 0:01) and team potency (g ¼ 0:732, p , 0:01). Personal network, however, was related neither to team efficacy (g ¼ 0:035, ns) nor to team potency (g ¼ 0:093, ns). The final two steps in our mediation analyses, shown in Table II, included both the mediator and the independent variables in the analyses. However, although tests for mediation hypotheses are most often guided by the four step classic approach proposed by Baron and Kenny (1986), the requirements are often difficult to be meet. For instance, the requirement that there be a significant independent to dependent variable relation in the Baron and Kenny model severely reduces power to detect mediation. There are many cases in which mediation can be demonstrated even when the requirement of a significant association between the independent and dependent variable is not met (as is the case in our study) (Cole et al., 2008; MacKinnon et al., 2007). In such cases, it is recommended that mediational analyses be based on formal significance testing, e.g. the Sobel (1982) with bootstrapping which is a significantly more powerful procedure. Through the application of bootstrapping it is possible to avoid power problems (see for an example Cole et al., 2008). To determine Sobel’s test statistics with bootstrapping, we used the STATA Sobel-Goodman Mediation Module of Ender (2008). Results from the Sobel tests indicate that the effect of personal network on team efficacy is mediated 87 percent by team learning behaviors (z ¼ 0:13, p , 0:01) and that the effect of personal network on team potency is mediated 45 percent by team learning behaviors (z ¼ 0:09, p , 0:01). H1 was supported, as these Sobel tests point at the positive association of personal network with both perceived team efficacy and team potency to be partly mediated by team learning behaviors. Further, the results from the Sobel tests indicate that the effect of deep-level similarity on team efficacy is mediated 44 percent by team learning behaviors (z ¼ 0:38, p , 0:01) and that the effect of deep level similarity on team potency is mediated 30 percent by team learning behaviors (z ¼ 0:25, p , 0:05). Thereby, H2 was supported, as the Sobel tests showed support for the positive association of deep level similarity with perceived team efficacy and team potency to be partly mediated by team learning behaviors. Finally, we tested H3 that social capital based on deep-level similarity interacts with social capital based on personal network to influence team efficacy and team potency, such that the association between social capital based on personal networks and team learning behaviors is stronger at higher levels of perceived deep-level similarity than at


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lower levels of perceived deep-level similarity. The hypothesis was tested using hierarchical regression analyses with team learning behaviors as the dependent variable. The interaction terms were created by centering the predictor variables (the mean was set to zero, standard deviation left unaffected) as recommended by Aiken and West (1991). From Table II, it can be seen that the expected interaction between social capital based on deep-level similarity and social capital based on personal networks indeed exists (g ¼ 20:59, p , 0:01) although not in the expected direction. Figure 1 graphically depicts the interaction. In teams scoring high on deep-level similarity, the level of team learning behaviors is higher than in teams scoring low on deep-level similarity, but is hardly dependent on the extent of social capital based on personal networks. For diverse teams (i.e. teams scoring low on deep-level similarity) more social capital based on personal networks translates into more team learning behaviors. Discussion The purpose of this study was to advance our understanding of the precursors to team efficacy and team potency, since these outcomes address beliefs in team capability. We argued that social capital generated through personal networks and social capital based on deep-level similarity will be associated with more team learning behaviors and also directly contribute to perceptions of team efficacy and team potency. Further, we found that in highly (deep-level) similar teams, the level of team learning behaviors is higher than in diverse teams, and is hardly dependent on the extent of social capital based on personal networks. For diverse teams (i.e. teams scoring low on deep-level similarity) more social capital based on personal networks translates into more team learning behaviors. Finally, we found that team learning behaviors mediate the influence of social capital on team efficacy and team potency. Our results support the contention that team learning behaviors are mechanisms (partially) mediating the relationship between social capital based on different mechanisms and team efficacy and team potency. Thus, rather than viewing social resource generation as directly affecting team efficacy and potency (see for example Leana and Pil, 2006; Oh et al., 2006), we placed the construct of team learning behaviors as an intervening variable between social capital and team capabilities. Team learning behaviors partially mediated all of the associations between resource generation from personal networks and from deep-level similarity. The results of the present study help to explain how the generation of resources, through deep-level similarity among team members and through networks, promote team capabilities by positively impacting team learning behaviors. Theoretical and practical implications Generating resources leads to more team learning behaviors and team learning behaviors are positively related to perceptions of team efficacy and team potency. This mediating or intervening role of team learning behavior as examined in the present study, can also be found in previous studies showing that process variables (e.g. Simons et al., 1999; Van Der Vegt and Bunderson, 2005) can mediate the relationship between independent variables and team functioning and effectiveness. Our results are consistent with empirical evidence suggesting that when team members engage in team learning behaviors, teams will be more effective (Edmondson, 1999; Van Der Vegt

and Bunderson, 2005). We stressed the importance of resource generation in a team setting. Opportunities to generate resources appear to be particularly important in team settings because working in teams increases the interdependence among workers (Campion and Medsker, 1993) and interpersonal relationship gain in importance in such settings. Although team efficacy and potency are depicted as outcomes in our research model, we feel that these capabilities reflect a process rather than an end-state (Sundstrom et al., 1990). This sequence of effects can be conceptualized as an ongoing process, whereby groups evaluate their performance as they work, and these evaluations affect group processes, which influence subsequent performance. Such a sequence could very well lead to self-reinforcing spirals of increasing team efficacy and team potency (see Sundstrom et al., 1990). Most previous research has emphasized surface-level variables, perhaps due to the ease with which surface variables, e.g. age and gender, can be measured (see Harrison et al., 1998). Researchers have also frequently used similarity on surface-level characteristics as surrogates or proxies for similarity on deep characteristics, but there is a growing acknowledgement about the importance of deep-level similarity (Goldberg et al., 2003). In the present study, we used deep-level similarity as more recent research indicates that the influence of deep-level similarity, but not surface-level similarity, increases with time. Deep-level similarity is likely to be most appropriate type of similarity measure for use with established ongoing teams in organizations. One of the most important contributions that SIT has made to the literature on organizational behavior is that a psychological group (team) is more than an extension of interpersonal relationships (Ashforth and Mael, 1989). Our study refined SIT by emphasizing the role of social capital and, specifically, deep-level similarity in interpersonal relationships and suggests that increasing the level of perceived deep-level similarity among group members improves team learning behaviors and subsequent team capabilities. Finally, we found that in similar teams, the level of team learning behaviors is hardly dependent on the extent of social capital based on personal networks. For diverse teams (i.e. teams scoring low on deep-level similarity) more social capital based on personal networks translates into more team learning behaviors. Apparently, it pays off to actively promote the development of social capital based on personal networks in diverse teams. Organizations that want to boost team learning can benefit by emphasizing the use of human resource systems that foster the development of informal relationship ties in both similar, but especially in diverse teams. Future research and limitations The present study has several implications for practitioners who manage multidisciplinary work teams and career management. First, our findings suggest that it is important for managers not to focus exclusively on surface level characteristics but instead attempt to facilitate the development of deep-level similarity, e.g. career management can benefit from deep-level similarity. Organizations can also encourage group social capital by allowing teams to develop a shared history, rather than changing membership frequently and by increasing contact among team members (Van Der Vegt and Bunderson, 2005). Further, since we


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know from SIT and social capital theory that employees generally are more attracted to and feel more comfortable interacting with others whom they perceive to be similar, interventions designed to help team members discover deep-level similarities should lead to more team learning behaviors (Phillips et al., 2006). Our study also has implications for career management and personnel selection. For instance, greater reliance upon teamwork and the benefits of team learning may require changes in promotion criteria and how careers are most effectively shaped within today’s organizations (see also Thite, 2001). Further, team-member selection tends to focus on identifying individual-level team-related competences or the extent to which an individual demonstrates competence in the necessary skills for teamwork in general. Examples of such skills include “teamwork”, “interpersonal sensitivity/awareness,” and “cooperativeness”. Our results suggest that this traditional approach might be complemented by considering (and thus measuring) the extent to which candidates share deep-level characteristics (e.g. values, norms, abilities) with current team members. Future research could also examine whether the mediating role of team learning behaviors is contingent on team structure or team size. For instance, regarding team structure, specific structures have been reported to be more beneficial for the acquisition of knowledge and skill (Ellis et al., 2003). Some words of caution regarding the results of this study are necessary. Data were collected with a single administration of a survey, raising concerns of common method variance. We feel that common method variance may be a less likely explanation because team learning behaviors of teachers were assessed by school management. It is also important to note that like much of the existing research in this area (Edmondson, 1999; Van Der Vegt and Bunderson, 2005), we also relied on perceptual measures because of the perceptual nature of the constructs investigated. Further, we used a cross-sectional design that cannot predict long-term team development. The variables of interest in our study may also exhibit temporal effects. For example, changes in social capital might affect team efficacy, and vice versa. Future research using longitudinal designs would strengthen the inferences drawn in the present study. Another limitation of this study is the reliance on schools and this limits generalizability. Replication of the present study should be conducted in a variety of other organizational settings. Lastly, our choice of variables to include in the present study has led to the exclusion of other variables. For instance, we examined how personal relationships and deep-level similarity as indicators of social capital relate to team learning behaviors and to team efficacy and team potency. In focusing on these relationships, we omitted a number of possible mediating and/or moderating variables. For instance, we did not include formal group structures or the influence of leadership on team learning behaviors; both of these constructs are worthy of study. Further, more research is necessary to fully understand complex constructs, such as social capital and team learning behaviors. Conclusion Notwithstanding these limitations, the results of our study indicate that social capital characteristics and team learning are important determinants of team success (Bell, 2007; Gully et al., 2002) and our results confirm the importance of team learning behaviors (Van den Bossche et al., 2006). In the present study, we examined exchange

and identification processes that are important in generating resources to increase the development of team learning behaviors, thereby emphasizing the role of the interpersonal context for understanding how interaction processes between team members shape team learning behaviors and subsequently lead to more team efficacy and team potency. References Adler, P.S. and Kwon, S.W. (2002), “Social capital: prospects for a new concept”, Academy of Management Review, Vol. 27 No. 1, pp. 17-40. Aiken, L.S. and West, S.G. (1991), Multiple Regression, Sage, Newbury Park, CA. Altman, D.G. (1991), Practical Statistics for Medical Research, Chapman & Hall, London. Ashforth, B.E. and Mael, F. (1989), “Social identity theory and the organization”, Academy of Management Review, Vol. 14 No. 1, pp. 20-39. Bandura, A. (1997a), Self-Efficacy: The Exercise of Control, Freeman, New York, NY. Bandura, A. (1997b), Social Learning Theory, Freeman, New York, NY. Baron, R.M. and Kenny, D.A. (1986), “The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations”, Journal of Personality and Social Psychology, Vol. 51 No. 6, pp. 1173-82. Beehr, T.A., Ivanitskaya, L., Hansen, C.P., Erofeev, D. and Gudanowski, D.M. (2001), “Evaluation of 360 degree feedback ratings: relationships with each other and with performance and selection predictors”, Journal of Organizational Behavior, Vol. 22 No. 7, pp. 775-88. Bell, S.T. (2007), “Deep-level composition variables as predictors of team performance: a meta-analysis”, Journal of Applied Psychology, Vol. 92 No. 3, pp. 595-615. Bradley, J., White, B.J. and Mennecke, B.E. (2003), “Teams and tasks: a temporal framework for the effects of interpersonal interventions on team performance”, Small Group Research, Vol. 34 No. 3, pp. 353-87. Campion, M.A. and Medsker, G.J. (1993), “Relations between work group characteristics and effectiveness: implications for designing effective work groups”, Personnel Psychology, Vol. 46 No. 4, pp. 823-50. Cole, M.S., Walter, F. and Bruch, H. (2008), “Affective mechanisms linking dysfunctional behavior to performance in work teams: a moderated mediation study”, Journal of Applied Psychology, Vol. 93 No. 5, pp. 945-58. Cummings, T.G. and Worley, C.G. (2005), Organization Development and Change, Thomson, Mason, OH. Edmondson, A. (1999), “Psychological safety and learning behavior in work teams”, Administrative Science Quarterly, Vol. 44 No. 2, pp. 350-83. Ellis, A.P.J., Hollenbeck, J.R., Ilgen, D.R., Porter, C.O.L.H., West, B. and Moon, H. (2003), “Team learning: collectively connecting the dots”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 821-35. Ender, P. (2008), “Sgmediation: Sobel-Goodman mediation tests”, UCLA: Academic Technology Services, Statistical Consulting Group, available at: www.ats.ucla.edu/stat/stata/ado/ analysis/ Gellert, F.J. and Kuipers, B.S. (2008), “Short- and long-term consequences of age in work teams: an empirical exploration of ageing teams”, Career Development International, Vol. 13 No. 2, pp. 132-49.


95

CDI 16,1

96

Gevers, J.M.P. (2004), “It’s about time we align: meeting deadlines in project teams”, unpublished doctoral thesis, Technical University Eindhoven, Eindhoven. Gibson, C.B. (1999), “Do they do what they believe they can? Group efficacy and group effectiveness across tasks and cultures”, Academy of Management Journal, Vol. 42 No. 2, pp. 138-52. Gist, M.E., Locke, E.A. and Taylor, M.S. (1987), “Organizational behavior: group structure, process, and effectiveness”, Journal of Management, Vol. 13 No. 2, pp. 237-57. Goldberg, C.B., Riordan, C.M. and Schaffer, B.S. (2003), “Missing pieces in social identity theory: continuity and status as moderators of similarity”, paper presented at the 63rd Annual Meeting of the Academy of Management, Seattle, August 1-6. Gully, S.M., Incalcaterra, K.A., Joshi, A. and Beaubien, J. (2002), “A meta-analysis of team-efficacy, potency, and performance: interdependence and level of analysis as moderators of observed relationships”, Journal of Applied Psychology, Vol. 87 No. 5, pp. 819-32. Guzzo, R.A. and Shea, G.P. (1992), “Group performance and intergroup relations in organizations”, in Dunnette, M.D. and Hough, L.M. (Eds), Handbook of Industrial and Organizational Psychology, Vol. 3, Consulting Psychologists Press, Palo Alto, CA, pp. 269-313. Guzzo, R.A., Yost, P.R., Campbell, R.J. and Shea, G.P. (1993), “Potency in groups: articulating a construct”, British Journal of Social Psychology, Vol. 32 No. 1, pp. 87-106. Harrison, D.A., Price, K.H. and Bell, M.P. (1998), “Beyond relational demography: time and the effects of surface- and deep-level diversity on work group cohesion”, Academy of Management Journal, Vol. 41 No. 1, pp. 96-107. Harrison, D.A., Price, K.H., Gavin, J.H. and Florey, A.T. (2002), “Time, teams, and task performance: changing effects of surface- and deep-level diversity on group functioning”, Academy of Management Journal, Vol. 45 No. 5, pp. 1029-45. Hausman, J.A. (1978), “Specification tests in econometrics”, Econometrica, Vol. 46 No. 6, pp. 1251-71. Ibarra, H. (1995), “Race, opportunity, and diversity of social circles in managerial networks”, Academy of Management Journal, Vol. 38 No. 3, pp. 673-703. Janssen, O., Van de Vliert, E. and Veenstra, C. (1999), “How task and person conflict shape the role of positive interdependence in management teams”, Journal of Management, Vol. 25 No. 2, pp. 117-41. Jex, S.M. (2002), Organizational Psychology: A Scientist-Practioner Approach, Wiley, New York, NY. Katzenbach, J.R. and Smith, D.K. (1993), The Wisdom of Teams. Creating the High-Performance Organization, Harvard Business School, Boston, MA. Kennedy, F.A., Loughry, M.L., Klammer, T.P. and Beyerlein, M.M. (2009), “Effects of organizational support on potency in work teams: the mediating role of team processes”, Small Group Research, Vol. 40 No. 1, pp. 72-93. Klein, K.J., Lim, B.C., Saltz, J.L. and Mayer, D.M. (2004), “How do they get there? An examination of the antecedents of centrality in team networks”, Academy of Management Journal, Vol. 47 No. 6, pp. 952-63. Kozlowski, S.W.J. and Ilgen, D.R. (2006), “Enhancing the effectiveness of work groups and teams”, Psychological Science in the Public Interest, Vol. 7 No. 3, pp. 77-124. Leana, C.R. and Pil, F.K. (2006), “Social capital and organizational performance: evidence from urban public schools”, Organization Science, Vol. 17 No. 3, pp. 353-66.

Lindsley, D.H., Brass, D.J. and Thomas, J.B. (1995), “Efficacy-performance spirals: a multilevel perspective”, Academy of Management Review, Vol. 20 No. 3, pp. 645-78. Longenecker, C.O. and Fink, L.S. (2008), “Key criteria in twenty-first century management promotional decisions”, Career Development International, Vol. 13 No. 3, pp. 241-51. McGrath, J.E. (1984), Groups: Interaction and Process, Prentice-Hall, Englewood Cliffs, NJ. MacKinnon, D.P., Fairchild, A.J. and Fritz, M.S. (2007), “Mediation analysis”, Annual Review of Psychology, Vol. 58, pp. 593-614. Mathieu, J., Maynard, M.T., Rapp, T. and Gilson, L. (2008), “Team effectiveness 1997-2007: a review of recent advancements and a glimpse into the future”, Journal of Management, Vol. 34 No. 3, pp. 410-76. Nahapiet, J. and Ghoshal, S. (1998), “Social capital, intellectual capital, and the organizational advantage”, Academy of Management Review, Vol. 23 No. 2, pp. 242-66. Oh, H., Labianca, G. and Myung-Ho, C. (2006), “A multilevel model of group social capital”, Academy of Management Review, Vol. 31 No. 3, pp. 569-82. Phillips, K.W., Northcraft, G.B. and Neale, M.A. (2006), “Surface-level diversity and decision making in groups: when does deep-level similarity help?”, Group Processes Intergroup Relations, Vol. 9 No. 4, pp. 467-82. Raider, H.J. and Burt, R.S. (1996), “Boundaryless careers and social capital”, in Arthur, M.B. and Rousseau, D. (Eds), The Boundaryless Career. A New Employment Principle for a New Organizational Era, Oxford University Press, New York, NY, pp. 187-200. Rowe, R.G. (1996), “Towards a team-based approach”, Career Development International, Vol. 1 No. 2, pp. 19-24. Salas, E., Rozell, D. and Mullen, B. (1999), “The effect of team building on performance”, Small Group Research, Vol. 30 No. 3, pp. 309-29. Sandefur, R.L. and Laumann, E.O. (1998), “A paradigm for social capital”, Rationality and Society, Vol. 10 No. 4, pp. 481-501. Schelfhaudt, K. and Crittenden, V.L. (2005), “Specialist or generalist? Views from academia and industry”, Journal of Business Research, Vol. 58 No. 7, p. 946. Seibert, S.E., Kraimer, M.L. and Liden, R.C. (2001), “A social capital theory of career success”, Academy of Management Journal, Vol. 44 No. 2, pp. 219-37. Shea, G.P. and Guzzo, R.A. (1987), “Group effectiveness: what really matters”, Sloan Management Review, Vol. 28, pp. 25-31. Simons, T., Pelled, L.H. and Smith, K.A. (1999), “Making use of difference: diversity, debate, and decision comprehensiveness in top management teams”, Academy of Management Journal, Vol. 42 No. 6, pp. 662-73. Sobel, M.E. (1982), “Asymptotic confidence intervals for indirect effects in structural equation models”, Sociological Methodology, Vol. 13, pp. 290-312. Sparrowe, R.T., Liden, R.C., Wayne, S.J. and Kraimer, M.L. (2001), “Social networks and the performance of individuals and groups”, Academy of Management Journal, Vol. 44 No. 2, pp. 316-25. Spielberger, C.D. (2002), Encyclopedia of Applied Psychology, Elsevier Academic Press, Oxford. Straits, B.C. (2000), “Ego’s important discussants or significant people: an experiment in varying the wording of personal network name generators”, Social Networks, Vol. 22 No. 2, pp. 123-40. Sundstrom, E., De Meuse, K.P. and Futrell, D. (1990), “Work teams: applications and effectiveness”, American Psychologist, Vol. 45 No. 3, pp. 120-33.


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Tajfel, H. (1982), “Social psychology of intergroup relations”, Annual Review of Psychology, Vol. 33 No. 1, pp. 1-39. Thite, M. (2001), “Help us but help yourself: the paradox of contemporary career management”, Career Development International, Vol. 6 No. 6, pp. 312-17. Turban, D.B., Dougherty, T.W. and Lee, F.K. (2002), “Gender, race, and perceived similarity effects in developmental relationships: the moderating role of relationship duration”, Journal of Vocational Behavior, Vol. 61 No. 2, pp. 240-62. Van den Bossche, P., Gijselaers, W.H., Segers, M. and Kirschner, P.A. (2006), “Social and cognitive factors driving teamwork in collaborative learning environments: team learning beliefs and behaviors”, Small Group Research, Vol. 37 No. 5, pp. 490-521. Van der Heijden, B. (2001), “Encouraging professional development in small and medium-sized firms. The influence of career history and job content”, Career Development International, Vol. 6 No. 3, pp. 156-68. Van Der Vegt, G.S. and Bunderson, J.S. (2005), “Learning and performance in multidisciplinary teams: the importance of collective team identification”, Academy of Management Journal, Vol. 48 No. 3, p. 532. Van Dick, R., Wagner, U., Stellmacher, J., Christ, O. and Tissington, P.A. (2005), “To be(long) or not to be(long): social identification in organizational contexts”, Genetic, Social and General Psychology Monographs, Vol. 131 No. 3, pp. 189-218. Van Emmerik, IJ.H. (2008), “It’s not only mentoring: the combined influences of individual-level and team-level support on job performance”, Career Development International, Vol. 13 No. 7, pp. 575-93. Zellmer-Bruhn, M. and Gibson, C. (2006), “Multinational organization context: implications for team learning and performance”, Academy of Management Journal, Vol. 49 No. 3, pp. 501-18. About the authors Hetty Van Emmerik, PhD Business Administration at Amsterdam (1991), is a Full Professor of Organizational Theory and Organizational Behavior in the Department of Organization and Strategy at Maastricht University, The Netherlands. Her research interests broadly include social relationships in the working context (e.g. leadership, teams, mentoring, networking, and social support issues) and the associations with various career outcomes (e.g. satisfaction, commitment, burnout, and work engagement). She has published in various journals, such as Career Development International, Group and Organization Management, Human Resource Management, Journal of Managerial Psychology, Work and Stress, Work and Occupations. Hetty Van Emmerik is the corresponding author and can be contacted at: h.vanemmerik@ maastrichtuniversity.nl I.M. “Jim” Jawahar (PhD, Oklahoma State University) is a Professor of Management and the Chairperson of the Department of Management and Quantitative Methods at Illinois State University. His primary research interest areas include performance appraisal, fairness, citizenship and counterproductive behaviors and stress. His research has appeared in journals including the Academy of Management Review, Journal of Applied Psychology, Personnel Psychology, Journal of Labor Research, Journal of Management and Human Performance, Human Relations and Group and Organization Management. He serves on the Editorial Boards of several journals. Bert H.J. Schreurs currently is Associate Professor in the Department of Organization and Strategy, Maastricht University School of Business and Economics. He received his PhD in Psychology from the University of Leuven, and previously has taught at Utrecht University and Hogeschool-Universiteit Brussels. His research broadly focuses on work stress and wellbeing,

leadership and group processes, and motivation and self-regulation. He previously worked as an adviser for the military on HRM topics such as recruitment, selection, and retention. He has published in various journals, such as Journal of Organizational Behavior, Human Performance, and Work & Stress. Nele De Cuyper, PhD, is an assistant professor at the Research Group Work, Organizational and Personnel Psychology, University of Leuven, Belgium. Her research interests incude contingent employment (e.g, temporary employment, fixed-term employment, temporary agency work), job insecurity, employability, the psychological contract and the career in general. She has published in journals such as Work and Stress, Journal of Occupational and Organizational Psychology, and Journal of Occupational Health Psychology.

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